
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_							:    Dict         =						logging.get_logger(__name__)

lowercase_							:    Any         =						{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class       UpperCamelCase   (				UpperCAmelCase__	):
     A__			  =      """roberta-prelayernorm"""
     def __init__(  self	,  snake_case__=50265	,  snake_case__=768	,  snake_case__=12	,  snake_case__=12	,  snake_case__=3072	,  snake_case__="gelu"	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=512	,  snake_case__=2	,  snake_case__=0.02	,  snake_case__=1E-12	,  snake_case__=1	,  snake_case__=0	,  snake_case__=2	,  snake_case__="absolute"	,  snake_case__=True	,  snake_case__=None	,  **snake_case__	,  ):




       """simple docstring"""


       super().__init__(pad_token_id=_a	,  bos_token_id=_a	,  eos_token_id=_a	,  **_a					)

       _SCREAMING_SNAKE_CASE :					Dict				     =			vocab_size
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			hidden_size
       _SCREAMING_SNAKE_CASE :					List[str]				     =			num_hidden_layers
       _SCREAMING_SNAKE_CASE :					int				     =			num_attention_heads
       _SCREAMING_SNAKE_CASE :					int				     =			hidden_act
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			intermediate_size
       _SCREAMING_SNAKE_CASE :					str				     =			hidden_dropout_prob
       _SCREAMING_SNAKE_CASE :					str				     =			attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE :					int				     =			max_position_embeddings
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			type_vocab_size
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			initializer_range
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			layer_norm_eps
       _SCREAMING_SNAKE_CASE :					Any				     =			position_embedding_type
       _SCREAMING_SNAKE_CASE :					str				     =			use_cache
       _SCREAMING_SNAKE_CASE :					Any				     =			classifier_dropout
class       UpperCamelCase   (				UpperCAmelCase__	):
     @property
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


       if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			{0: "batch", 1: "choice", 2: "sequence"}
       else:
         _SCREAMING_SNAKE_CASE :					Dict				     =			{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]					)









"""simple docstring"""




def        a__					(							snake_case__ = 10_00							)		->   int:
			lowerCamelCase ,  lowerCamelCase      =   1, 1
			lowerCamelCase      =   2
			while True:
						lowerCamelCase      =   0
						lowerCamelCase      =   fa + fa
						lowerCamelCase ,  lowerCamelCase      =   fa, f
						index += 1
						for _ in str(snake_case__							):
									i += 1
						if i == n:
									break
			return index


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))



